SELF-ORGANIZATION OF FIRING ACTIVITIES IN MONKEYS MOTOR CORTEX - TRAJECTORY COMPUTATION FROM SPIKE SIGNALS

The population vector method has been developed to combine the simulta neous direction-related activities of a population of motor cortical n eurons to predict the trajectory of the arm movement. In this article, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. A self-organizing f eature map (SOFM) is used to select the optimal set of weights in the model to determine the contribution of an individual neuron to an over all movement representation. The correspondence between movement direc tions and discharge patterns of the motor cortical neurons is establis hed in the output map. The topology-preserving property of the SOFM is used to analyze the recorded data of a behaving monkey. The data used in this analysis were taken while the monkey was tracing spirals and doing center --> out movements. The arm trajectory could be well predi cted using such a statistical model based on the motor cortex neuronal firing information. The SOFM method is compared with the population v ector method, which extracts information related to trajectory by assu ming that each cell has a fixed preferred direction during the task. T his implies that these cells are acting along lines labeled only for d irection. However, extradirectional information is carried in these ce ll responses. The SOFM has the capability of extracting not only direc tion-related information but also other parameters that are consistent ly represented in the activity of the recorded population of cells.